Examples of this form of network include [1]}, [2]}, [3]}, [4]}. The key concept in multi-stream network is illustrated in Fig. REF (b). This kind of network pursues learning multi-scale saliency information via training multiple networks with multi-scale inputs. The multiple network streams are parallel and may have different architectures, corresponding to multiple scales.
As demonstrated in [5]}, input data are simultaneously fed into multiple
streams, and then the feature responses from various streams are concatenated and fed into a global output
layer to produce the final saliency.
We can find that, with multi-stream network, the multi-scale learning happens “outside” the networks. In the following architecture, the multi-scale or multi-level learning is “inside” the network, which is achieved via combining hierarchical features from multiple convolutional layers.
